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You can ask aGeminimodel to generate text from a text-only prompt or
a multimodal prompt. When you useFirebase AI Logic, you can make this
request directly from your app.
Multimodal prompts can include multiple types of input
(like text along with images, PDFs, plain-text files, audio, and video).
This guide shows how to generate text from a text-only prompt and from a basic
multimodal prompt that includes a file.
Click yourGemini APIprovider to view provider-specific content
and code on this page.
If you haven't already, complete thegetting started guide, which describes how to
set up your Firebase project, connect your app to Firebase, add the SDK,
initialize the backend service for your chosenGemini APIprovider, and
create aGenerativeModelinstance.
For testing and iterating on your prompts and even
getting a generated code snippet, we recommend usingGoogle AI Studio.
Generate text from text-only input
Before trying this sample, complete theBefore you beginsection of this guide
to set up your project and app. In that section, you'll also click a button for your chosenGemini APIprovider so that you see provider-specific content
on this page.
You can ask aGeminimodel to generate text by prompting with text-only
input.
importFirebaseAI// Initialize the Gemini Developer API backend serviceletai=FirebaseAI.firebaseAI(backend:.googleAI())// Create a `GenerativeModel` instance with a model that supports your use caseletmodel=ai.generativeModel(modelName:"gemini-2.5-flash")// Provide a prompt that contains textletprompt="Write a story about a magic backpack."// To generate text output, call generateContent with the text inputletresponse=tryawaitmodel.generateContent(prompt)print(response.text??"No text in response.")
For Kotlin, the methods in this SDK are suspend functions and need to be called
from aCoroutine scope.
// Initialize the Gemini Developer API backend service// Create a `GenerativeModel` instance with a model that supports your use casevalmodel=Firebase.ai(backend=GenerativeBackend.googleAI()).generativeModel("gemini-2.5-flash")// Provide a prompt that contains textvalprompt="Write a story about a magic backpack."// To generate text output, call generateContent with the text inputvalresponse=generativeModel.generateContent(prompt)print(response.text)
// Initialize the Gemini Developer API backend service// Create a `GenerativeModel` instance with a model that supports your use caseGenerativeModelai=FirebaseAI.getInstance(GenerativeBackend.googleAI()).generativeModel("gemini-2.5-flash");// Use the GenerativeModelFutures Java compatibility layer which offers// support for ListenableFuture and Publisher APIsGenerativeModelFuturesmodel=GenerativeModelFutures.from(ai);// Provide a prompt that contains textContentprompt=newContent.Builder().addText("Write a story about a magic backpack.").build();// To generate text output, call generateContent with the text inputListenableFuture<GenerateContentResponse>response=model.generateContent(prompt);Futures.addCallback(response,newFutureCallback<GenerateContentResponse>(){@OverridepublicvoidonSuccess(GenerateContentResponseresult){StringresultText=result.getText();System.out.println(resultText);}@OverridepublicvoidonFailure(Throwablet){t.printStackTrace();}},executor);
import{initializeApp}from"firebase/app";import{getAI,getGenerativeModel,GoogleAIBackend}from"firebase/ai";// TODO(developer) Replace the following with your app's Firebase configuration// See: https://firebase.google.com/docs/web/learn-more#config-objectconstfirebaseConfig={// ...};// Initialize FirebaseAppconstfirebaseApp=initializeApp(firebaseConfig);// Initialize the Gemini Developer API backend serviceconstai=getAI(firebaseApp,{backend:newGoogleAIBackend()});// Create a `GenerativeModel` instance with a model that supports your use caseconstmodel=getGenerativeModel(ai,{model:"gemini-2.5-flash"});// Wrap in an async function so you can use awaitasyncfunctionrun(){// Provide a prompt that contains textconstprompt="Write a story about a magic backpack."// To generate text output, call generateContent with the text inputconstresult=awaitmodel.generateContent(prompt);constresponse=result.response;consttext=response.text();console.log(text);}run();
import'package:firebase_ai/firebase_ai.dart';import'package:firebase_core/firebase_core.dart';import'firebase_options.dart';// Initialize FirebaseAppawaitFirebase.initializeApp(options:DefaultFirebaseOptions.currentPlatform,);// Initialize the Gemini Developer API backend service// Create a `GenerativeModel` instance with a model that supports your use casefinalmodel=FirebaseAI.googleAI().generativeModel(model:'gemini-2.5-flash');// Provide a prompt that contains textfinalprompt=[Content.text('Write a story about a magic backpack.')];// To generate text output, call generateContent with the text inputfinalresponse=awaitmodel.generateContent(prompt);print(response.text);
usingFirebase;usingFirebase.AI;// Initialize the Gemini Developer API backend servicevarai=FirebaseAI.GetInstance(FirebaseAI.Backend.GoogleAI());// Create a `GenerativeModel` instance with a model that supports your use casevarmodel=ai.GetGenerativeModel(modelName:"gemini-2.5-flash");// Provide a prompt that contains textvarprompt="Write a story about a magic backpack.";// To generate text output, call GenerateContentAsync with the text inputvarresponse=awaitmodel.GenerateContentAsync(prompt);UnityEngine.Debug.Log(response.Text??"No text in response.");
Learn how to choose amodelappropriate for your use case and app.
Generate text from text-and-file (multimodal) input
Before trying this sample, complete theBefore you beginsection of this guide
to set up your project and app. In that section, you'll also click a button for your chosenGemini APIprovider so that you see provider-specific content
on this page.
You can ask aGeminimodel to
generate text by prompting with text and a file—providing each
input file'smimeTypeand the file itself. Findrequirements and recommendations for input fileslater on this page.
The following example shows the basics of how to generate text from a file input
by analyzing a single video file provided as inline data (base64-encoded file).
Note that this example shows providing the file inline, but the SDKs also
supportproviding a YouTube URL.
Need a sample video file?
You can use this publicly available file with a MIME type ofvideo/mp4(view or download file).https://storage.googleapis.com/cloud-samples-data/video/animals.mp4
Swift
You can callgenerateContent()to generate text from multimodal input of text and video files.
importFirebaseAI// Initialize the Gemini Developer API backend serviceletai=FirebaseAI.firebaseAI(backend:.googleAI())// Create a `GenerativeModel` instance with a model that supports your use caseletmodel=ai.generativeModel(modelName:"gemini-2.5-flash")// Provide the video as `Data` with the appropriate MIME type.letvideo=InlineDataPart(data:tryData(contentsOf:videoURL),mimeType:"video/mp4")// Provide a text prompt to include with the videoletprompt="What is in the video?"// To generate text output, call generateContent with the text and videoletresponse=tryawaitmodel.generateContent(video,prompt)print(response.text??"No text in response.")
Kotlin
You can callgenerateContent()to generate text from multimodal input of text and video files.
For Kotlin, the methods in this SDK are suspend functions and need to be called
from aCoroutine scope.
// Initialize the Gemini Developer API backend service// Create a `GenerativeModel` instance with a model that supports your use casevalmodel=Firebase.ai(backend=GenerativeBackend.googleAI()).generativeModel("gemini-2.5-flash")valcontentResolver=applicationContext.contentResolvercontentResolver.openInputStream(videoUri).use{stream->stream?.let{valbytes=stream.readBytes()// Provide a prompt that includes the video specified above and textvalprompt=content{inlineData(bytes,"video/mp4")text("What is in the video?")}// To generate text output, call generateContent with the promptvalresponse=generativeModel.generateContent(prompt)Log.d(TAG,response.text?:"")}}
Java
You can callgenerateContent()to generate text from multimodal input of text and video files.
// Initialize the Gemini Developer API backend service// Create a `GenerativeModel` instance with a model that supports your use caseGenerativeModelai=FirebaseAI.getInstance(GenerativeBackend.googleAI()).generativeModel("gemini-2.5-flash");// Use the GenerativeModelFutures Java compatibility layer which offers// support for ListenableFuture and Publisher APIsGenerativeModelFuturesmodel=GenerativeModelFutures.from(ai);ContentResolverresolver=getApplicationContext().getContentResolver();try(InputStreamstream=resolver.openInputStream(videoUri)){FilevideoFile=newFile(newURI(videoUri.toString()));intvideoSize=(int)videoFile.length();byte[]videoBytes=newbyte[videoSize];if(stream!=null){stream.read(videoBytes,0,videoBytes.length);stream.close();// Provide a prompt that includes the video specified above and textContentprompt=newContent.Builder().addInlineData(videoBytes,"video/mp4").addText("What is in the video?").build();// To generate text output, call generateContent with the promptListenableFuture<GenerateContentResponse>response=model.generateContent(prompt);Futures.addCallback(response,newFutureCallback<GenerateContentResponse>(){@OverridepublicvoidonSuccess(GenerateContentResponseresult){StringresultText=result.getText();System.out.println(resultText);}@OverridepublicvoidonFailure(Throwablet){t.printStackTrace();}},executor);}}catch(IOExceptione){e.printStackTrace();}catch(URISyntaxExceptione){e.printStackTrace();}
Web
You can callgenerateContent()to generate text from multimodal input of text and video files.
import{initializeApp}from"firebase/app";import{getAI,getGenerativeModel,GoogleAIBackend}from"firebase/ai";// TODO(developer) Replace the following with your app's Firebase configuration// See: https://firebase.google.com/docs/web/learn-more#config-objectconstfirebaseConfig={// ...};// Initialize FirebaseAppconstfirebaseApp=initializeApp(firebaseConfig);// Initialize the Gemini Developer API backend serviceconstai=getAI(firebaseApp,{backend:newGoogleAIBackend()});// Create a `GenerativeModel` instance with a model that supports your use caseconstmodel=getGenerativeModel(ai,{model:"gemini-2.5-flash"});// Converts a File object to a Part object.asyncfunctionfileToGenerativePart(file){constbase64EncodedDataPromise=newPromise((resolve)=>{constreader=newFileReader();reader.onloadend=()=>resolve(reader.result.split(',')[1]);reader.readAsDataURL(file);});return{inlineData:{data:awaitbase64EncodedDataPromise,mimeType:file.type},};}asyncfunctionrun(){// Provide a text prompt to include with the videoconstprompt="What do you see?";constfileInputEl=document.querySelector("input[type=file]");constvideoPart=awaitfileToGenerativePart(fileInputEl.files[0]);// To generate text output, call generateContent with the text and videoconstresult=awaitmodel.generateContent([prompt,videoPart]);constresponse=result.response;consttext=response.text();console.log(text);}run();
Dart
You can callgenerateContent()to generate text from multimodal input of text and video files.
import'package:firebase_ai/firebase_ai.dart';import'package:firebase_core/firebase_core.dart';import'firebase_options.dart';// Initialize FirebaseAppawaitFirebase.initializeApp(options:DefaultFirebaseOptions.currentPlatform,);// Initialize the Gemini Developer API backend service// Create a `GenerativeModel` instance with a model that supports your use casefinalmodel=FirebaseAI.googleAI().generativeModel(model:'gemini-2.5-flash');// Provide a text prompt to include with the videofinalprompt=TextPart("What's in the video?");// Prepare video for inputfinalvideo=awaitFile('video0.mp4').readAsBytes();// Provide the video as `Data` with the appropriate mimetypefinalvideoPart=InlineDataPart('video/mp4',video);// To generate text output, call generateContent with the text and imagesfinalresponse=awaitmodel.generateContent([Content.multi([prompt,...videoPart])]);print(response.text);
Unity
You can callGenerateContentAsync()to generate text from multimodal input of text and video files.
usingFirebase;usingFirebase.AI;// Initialize the Gemini Developer API backend servicevarai=FirebaseAI.GetInstance(FirebaseAI.Backend.GoogleAI());// Create a `GenerativeModel` instance with a model that supports your use casevarmodel=ai.GetGenerativeModel(modelName:"gemini-2.5-flash");// Provide the video as `data` with the appropriate MIME type.varvideo=ModelContent.InlineData("video/mp4",System.IO.File.ReadAllBytes(System.IO.Path.Combine(UnityEngine.Application.streamingAssetsPath,"yourVideo.mp4")));// Provide a text prompt to include with the videovarprompt=ModelContent.Text("What is in the video?");// To generate text output, call GenerateContentAsync with the text and videovarresponse=awaitmodel.GenerateContentAsync(new[]{video,prompt});UnityEngine.Debug.Log(response.Text??"No text in response.");
Learn how to choose amodelappropriate for your use case and app.
Stream the response
Before trying this sample, complete theBefore you beginsection of this guide
to set up your project and app. In that section, you'll also click a button for your chosenGemini APIprovider so that you see provider-specific content
on this page.
You can achieve faster interactions by not waiting for the entire result from
the model generation, and instead use streaming to handle partial results.
To stream the response, callgenerateContentStream.
View example: Stream generated text from text-only input
importFirebaseAI// Initialize the Gemini Developer API backend serviceletai=FirebaseAI.firebaseAI(backend:.googleAI())// Create a `GenerativeModel` instance with a model that supports your use caseletmodel=ai.generativeModel(modelName:"gemini-2.5-flash")// Provide a prompt that contains textletprompt="Write a story about a magic backpack."// To stream generated text output, call generateContentStream with the text inputletcontentStream=trymodel.generateContentStream(prompt)fortryawaitchunkincontentStream{iflettext=chunk.text{print(text)}}
For Kotlin, the methods in this SDK are suspend functions and need to be called
from aCoroutine scope.
// Initialize the Gemini Developer API backend service// Create a `GenerativeModel` instance with a model that supports your use casevalmodel=Firebase.ai(backend=GenerativeBackend.googleAI()).generativeModel("gemini-2.5-flash")// Provide a prompt that includes only textvalprompt="Write a story about a magic backpack."// To stream generated text output, call generateContentStream and pass in the promptvarresponse=""generativeModel.generateContentStream(prompt).collect{chunk->print(chunk.text)response+=chunk.text}
For Java, the streaming methods in this SDK return aPublishertype from theReactive Streams library.
// Initialize the Gemini Developer API backend service// Create a `GenerativeModel` instance with a model that supports your use caseGenerativeModelai=FirebaseAI.getInstance(GenerativeBackend.googleAI()).generativeModel("gemini-2.5-flash");// Use the GenerativeModelFutures Java compatibility layer which offers// support for ListenableFuture and Publisher APIsGenerativeModelFuturesmodel=GenerativeModelFutures.from(ai);// Provide a prompt that contains textContentprompt=newContent.Builder().addText("Write a story about a magic backpack.").build();// To stream generated text output, call generateContentStream with the text inputPublisher<GenerateContentResponse>streamingResponse=model.generateContentStream(prompt);// Subscribe to partial results from the responsefinalString[]fullResponse={""};streamingResponse.subscribe(newSubscriber<GenerateContentResponse>(){@OverridepublicvoidonNext(GenerateContentResponsegenerateContentResponse){Stringchunk=generateContentResponse.getText();fullResponse[0]+=chunk;}@OverridepublicvoidonComplete(){System.out.println(fullResponse[0]);}@OverridepublicvoidonError(Throwablet){t.printStackTrace();}@OverridepublicvoidonSubscribe(Subscriptions){}});
import{initializeApp}from"firebase/app";import{getAI,getGenerativeModel,GoogleAIBackend}from"firebase/ai";// TODO(developer) Replace the following with your app's Firebase configuration// See: https://firebase.google.com/docs/web/learn-more#config-objectconstfirebaseConfig={// ...};// Initialize FirebaseAppconstfirebaseApp=initializeApp(firebaseConfig);// Initialize the Gemini Developer API backend serviceconstai=getAI(firebaseApp,{backend:newGoogleAIBackend()});// Create a `GenerativeModel` instance with a model that supports your use caseconstmodel=getGenerativeModel(ai,{model:"gemini-2.5-flash"});// Wrap in an async function so you can use awaitasyncfunctionrun(){// Provide a prompt that contains textconstprompt="Write a story about a magic backpack."// To stream generated text output, call generateContentStream with the text inputconstresult=awaitmodel.generateContentStream(prompt);forawait(constchunkofresult.stream){constchunkText=chunk.text();console.log(chunkText);}console.log('aggregated response: ',awaitresult.response);}run();
import'package:firebase_ai/firebase_ai.dart';import'package:firebase_core/firebase_core.dart';import'firebase_options.dart';// Initialize FirebaseAppawaitFirebase.initializeApp(options:DefaultFirebaseOptions.currentPlatform,);// Initialize the Gemini Developer API backend service// Create a `GenerativeModel` instance with a model that supports your use casefinalmodel=FirebaseAI.googleAI().generativeModel(model:'gemini-2.5-flash');// Provide a prompt that contains textfinalprompt=[Content.text('Write a story about a magic backpack.')];// To stream generated text output, call generateContentStream with the text inputfinalresponse=model.generateContentStream(prompt);awaitfor(finalchunkinresponse){print(chunk.text);}
usingFirebase;usingFirebase.AI;// Initialize the Gemini Developer API backend servicevarai=FirebaseAI.GetInstance(FirebaseAI.Backend.GoogleAI());// Create a `GenerativeModel` instance with a model that supports your use casevarmodel=ai.GetGenerativeModel(modelName:"gemini-2.5-flash");// Provide a prompt that contains textvarprompt="Write a story about a magic backpack.";// To stream generated text output, call GenerateContentStreamAsync with the text inputvarresponseStream=model.GenerateContentStreamAsync(prompt);awaitforeach(varresponseinresponseStream){if(!string.IsNullOrWhiteSpace(response.Text)){UnityEngine.Debug.Log(response.Text);}}
Learn how to choose amodelappropriate for your use case and app.
View example: Stream generated text from multimodal input
Swift
You can callgenerateContentStream()to stream generated text from multimodal input of text and a single video.
importFirebaseAI// Initialize the Gemini Developer API backend serviceletai=FirebaseAI.firebaseAI(backend:.googleAI())// Create a `GenerativeModel` instance with a model that supports your use caseletmodel=ai.generativeModel(modelName:"gemini-2.5-flash")// Provide the video as `Data` with the appropriate MIME typeletvideo=InlineDataPart(data:tryData(contentsOf:videoURL),mimeType:"video/mp4")// Provide a text prompt to include with the videoletprompt="What is in the video?"// To stream generated text output, call generateContentStream with the text and videoletcontentStream=trymodel.generateContentStream(video,prompt)fortryawaitchunkincontentStream{iflettext=chunk.text{print(text)}}
Kotlin
You can callgenerateContentStream()to stream generated text from multimodal input of text and a single video.
For Kotlin, the methods in this SDK are suspend functions and need to be called
from aCoroutine scope.
// Initialize the Gemini Developer API backend service// Create a `GenerativeModel` instance with a model that supports your use casevalmodel=Firebase.ai(backend=GenerativeBackend.googleAI()).generativeModel("gemini-2.5-flash")valcontentResolver=applicationContext.contentResolvercontentResolver.openInputStream(videoUri).use{stream->stream?.let{valbytes=stream.readBytes()// Provide a prompt that includes the video specified above and textvalprompt=content{inlineData(bytes,"video/mp4")text("What is in the video?")}// To stream generated text output, call generateContentStream with the promptvarfullResponse=""generativeModel.generateContentStream(prompt).collect{chunk->Log.d(TAG,chunk.text?:"")fullResponse+=chunk.text}}}
Java
You can callgenerateContentStream()to stream generated text from multimodal input of text and a single video.
For Java, the streaming methods in this SDK return aPublishertype from theReactive Streams library.
// Initialize the Gemini Developer API backend service// Create a `GenerativeModel` instance with a model that supports your use caseGenerativeModelai=FirebaseAI.getInstance(GenerativeBackend.googleAI()).generativeModel("gemini-2.5-flash");// Use the GenerativeModelFutures Java compatibility layer which offers// support for ListenableFuture and Publisher APIsGenerativeModelFuturesmodel=GenerativeModelFutures.from(ai);ContentResolverresolver=getApplicationContext().getContentResolver();try(InputStreamstream=resolver.openInputStream(videoUri)){FilevideoFile=newFile(newURI(videoUri.toString()));intvideoSize=(int)videoFile.length();byte[]videoBytes=newbyte[videoSize];if(stream!=null){stream.read(videoBytes,0,videoBytes.length);stream.close();// Provide a prompt that includes the video specified above and textContentprompt=newContent.Builder().addInlineData(videoBytes,"video/mp4").addText("What is in the video?").build();// To stream generated text output, call generateContentStream with the promptPublisher<GenerateContentResponse>streamingResponse=model.generateContentStream(prompt);finalString[]fullResponse={""};streamingResponse.subscribe(newSubscriber<GenerateContentResponse>(){@OverridepublicvoidonNext(GenerateContentResponsegenerateContentResponse){Stringchunk=generateContentResponse.getText();fullResponse[0]+=chunk;}@OverridepublicvoidonComplete(){System.out.println(fullResponse[0]);}@OverridepublicvoidonError(Throwablet){t.printStackTrace();}@OverridepublicvoidonSubscribe(Subscriptions){}});}}catch(IOExceptione){e.printStackTrace();}catch(URISyntaxExceptione){e.printStackTrace();}
Web
You can callgenerateContentStream()to stream generated text from multimodal input of text and a single video.
import{initializeApp}from"firebase/app";import{getAI,getGenerativeModel,GoogleAIBackend}from"firebase/ai";// TODO(developer) Replace the following with your app's Firebase configuration// See: https://firebase.google.com/docs/web/learn-more#config-objectconstfirebaseConfig={// ...};// Initialize FirebaseAppconstfirebaseApp=initializeApp(firebaseConfig);// Initialize the Gemini Developer API backend serviceconstai=getAI(firebaseApp,{backend:newGoogleAIBackend()});// Create a `GenerativeModel` instance with a model that supports your use caseconstmodel=getGenerativeModel(ai,{model:"gemini-2.5-flash"});// Converts a File object to a Part object.asyncfunctionfileToGenerativePart(file){constbase64EncodedDataPromise=newPromise((resolve)=>{constreader=newFileReader();reader.onloadend=()=>resolve(reader.result.split(',')[1]);reader.readAsDataURL(file);});return{inlineData:{data:awaitbase64EncodedDataPromise,mimeType:file.type},};}asyncfunctionrun(){// Provide a text prompt to include with the videoconstprompt="What do you see?";constfileInputEl=document.querySelector("input[type=file]");constvideoPart=awaitfileToGenerativePart(fileInputEl.files[0]);// To stream generated text output, call generateContentStream with the text and videoconstresult=awaitmodel.generateContentStream([prompt,videoPart]);forawait(constchunkofresult.stream){constchunkText=chunk.text();console.log(chunkText);}}run();
Dart
You can callgenerateContentStream()to stream generated text from multimodal input of text and a single video.
import'package:firebase_ai/firebase_ai.dart';import'package:firebase_core/firebase_core.dart';import'firebase_options.dart';// Initialize FirebaseAppawaitFirebase.initializeApp(options:DefaultFirebaseOptions.currentPlatform,);// Initialize the Gemini Developer API backend service// Create a `GenerativeModel` instance with a model that supports your use casefinalmodel=FirebaseAI.googleAI().generativeModel(model:'gemini-2.5-flash');// Provide a text prompt to include with the videofinalprompt=TextPart("What's in the video?");// Prepare video for inputfinalvideo=awaitFile('video0.mp4').readAsBytes();// Provide the video as `Data` with the appropriate mimetypefinalvideoPart=InlineDataPart('video/mp4',video);// To stream generated text output, call generateContentStream with the text and imagefinalresponse=awaitmodel.generateContentStream([Content.multi([prompt,videoPart])]);awaitfor(finalchunkinresponse){print(chunk.text);}
Unity
You can callGenerateContentStreamAsync()to stream generated text from multimodal input of text and a single video.
usingFirebase;usingFirebase.AI;// Initialize the Gemini Developer API backend servicevarai=FirebaseAI.GetInstance(FirebaseAI.Backend.GoogleAI());// Create a `GenerativeModel` instance with a model that supports your use casevarmodel=ai.GetGenerativeModel(modelName:"gemini-2.5-flash");// Provide the video as `data` with the appropriate MIME type.varvideo=ModelContent.InlineData("video/mp4",System.IO.File.ReadAllBytes(System.IO.Path.Combine(UnityEngine.Application.streamingAssetsPath,"yourVideo.mp4")));// Provide a text prompt to include with the videovarprompt=ModelContent.Text("What is in the video?");// To stream generated text output, call GenerateContentStreamAsync with the text and videovarresponseStream=model.GenerateContentStreamAsync(new[]{video,prompt});awaitforeach(varresponseinresponseStream){if(!string.IsNullOrWhiteSpace(response.Text)){UnityEngine.Debug.Log(response.Text);}}
Learn how to choose amodelappropriate for your use case and app.
Requirements and recommendations for input image files
Note that a file provided as inline data is encoded to base64 in transit, which
increases the size of the request. You get an HTTP 413 error if a request is
too large.
Different options for providing a file in a request
(either inline or using the file's URL or URI)
Supported file types
Supported MIME types and how to specify them
Requirements and best practices for files and multimodal requests
What else can you do?
Learn how tocount tokensbefore sending long prompts to the model.
Set upCloud Storage for Firebaseso that you can include large files in your multimodal requests and have a
more managed solution for providing files in prompts.
Files can include images, PDFs, video, and audio.
Start thinking about preparing for production (see theproduction checklist),
including:
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-09-05 UTC."],[],[],null,[]]
